Abstract 2151: Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders
Authors: Isabella Alt, Robert Sehlke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Klaus Hackner, Lucia Dzurillova, Edgar Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Jozef Šufliarsky, Lukas Hefler, Thorsten Füreder, Christina Taubert, Andrew Payne, Christophe Boudesco, Gregory Ian Vladimer
Journal: Cancer Research (2023)

Abstract Background: Precision cancer medicine aims to identify the right drug for the right patient, enriching for patients more likely to respond to a particular treatment. This paradigm is gaining importance during the early clinical lifecycle of a new potential drug to improve patient-centric trial designs, drive clinical success and eventually increase approval rates - enlarging the therapeutic arsenal available for oncology patients. To optimize the chance of success with our A2AR-selective antagonist, EXS-21546 (546; NCT04727138, discovered in collaboration with Evotec), we have identified an adenosine-induced immunosuppression biomarker signature (adenosine burden score or ABS) for clinical trial patient selection that also correlates with checkpoint inhibitor (CI) response prediction in ex vivo primary models. Here we present transcriptional and functional data mapping adenosine burden at the single cell level, and investigate subsequent modulation through antagonism of A2AR with 546, combination effects with CI, to prioritize patients for 546+CI therapy. Methods: By leveraging disease-relevant primary human tissues together with matched single cell and bulk transcriptomics, we assess adenosine-induced anticancer immune suppression and show initial biological confirmation of patient selection methodology and combination therapy effects with a translatable high content imaging platform (Kornauth et al 2021 &amp; Snidjer et al 2017). Results: The ABS detects adenosine rich microenvironments with greater specificity and sensitivity than other published signatures. Validating the ABS in TGCA, we found the ABS anti-correlates with a validated predictor of anti-PD-1 therapy success (TIS, Damotte et al 2019), unraveling that high-adenosine/ABS cases are among patients least likely to respond to immunotherapy (low TIS). A2AR antagonism with 546 demonstrated a reduction of the adenosine burden, and restored the CI response potential as addressed by the ABS and TIS, respectively. Further immune reactivation was seen with antagonism of adenosine signaling by ‘546/CI combination ex vivo in primary tissues pre-selected with our ABS signature. Conclusions: Combining deep learning of single cell functional and multi-omics profiling data of disease relevant primary model systems, we model the association of the immune response potential to A2AR antagonism in cancer to define a biomarker signature to predict patients likely to benefit from A2AR antagonism and CI. This will be confirmed and validated retrospectively in an ongoing clinical study of 546 in two cancer indications. Citation Format: Isabella Alt, Robert Sehlke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Klaus Hackner, Lucia Dzurillova, Edgar Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Jozef Šufliarsky, Lukas Hefler, Thorsten Füreder, Christina Taubert, Andrew Payne, Christophe Boudesco, Gregory Ian Vladimer. Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2151.